3 Coding attention mechanisms

Keywords

ver. 1.0.0, 3_coding_attention_mechanisms

Practical end-to-end construction of causal multi‑head self‑attention in PyTorch, from the core math to an efficient batched implementation

This unit teaches how attention replaces the RNN encoder‑decoder bottleneck and guides you step-by-step to implement causal multi‑head self‑attention in PyTorch: compute attention for one token, vectorize the computation for all tokens, add learned query/key/value projections and scaling, encapsulate the logic in nn.Module using nn.Linear, apply causal masking, add dropout regularization, handle batched inputs, and build an efficient multi‑head implementation suitable for GPT‑style models.

Full summary

Learners end the unit able to both explain and implement the attention mechanism used in modern transformer decoders. You will understand why RNN encoder‑decoder architectures suffer an information bottleneck and how attention removes it by letting every output position attend directly to relevant input positions. Starting from raw token embeddings, you will work through the three arithmetic steps of attention for a single query—score, normalize, sum—implemented without trainable parameters to reveal the mechanics.

From there you will vectorize the per‑token loop into two matrix operations that compute context vectors for all tokens at once. You will introduce trainable query, key and value projections and the 1/sqrt(d_k) scaling that stabilizes gradients, and learn how to package the whole computation as a reusable PyTorch layer using nn.Linear rather than raw parameters.

The unit shows how to make attention causal by masking future positions (setting their scores to −∞), so the layer can be used for next‑token prediction, and how to add dropout on attention weights while accounting for the rescaling needed to preserve expected activations. You will implement attention that accepts batched inputs with shape (batch, tokens, dimension), store masks as buffers, and assemble masking, dropout and batching into a CausalAttention module.

Finally you will build multi‑head causal attention: first the straightforward approach of running parallel heads and concatenating, then the efficient production form that projects once, reshapes to split heads, and batches the matrix multiplies. By the end you can write a production‑quality MultiHeadAttention module compatible with GPT‑style decoders and you will be prepared to use it as the core layer when implementing a full autoregressive language model.

Materials

Lecture notesThe unit written out, to read at your own pacever. 1.2.0 · 2026-09-02 16:27:30Open → SlidesThe deck to present fromver. 1.0.0 · 2026-08-25 14:45:14Open →

Source document

Building a Large Language Model (from scratch), Sebastian Raschka, 2024, Manning Books — Link — Page 72-113

← 2 Working with text data 4 Implementing a GPT model from scratch to generate text →

--- title: "3 Coding attention mechanisms" keywords: - ver. 1.0.0 - "3_coding_attention_mechanisms" format: html: toc: false --- **Practical end-to-end construction of causal multi‑head self‑attention in PyTorch, from the core math to an efficient batched implementation** > This unit teaches how attention replaces the RNN encoder‑decoder bottleneck and guides you step-by-step to implement causal multi‑head self‑attention in PyTorch: compute attention for one token, vectorize the computation for all tokens, add learned query/key/value projections and scaling, encapsulate the logic in nn.Module using nn.Linear, apply causal masking, add dropout regularization, handle batched inputs, and build an efficient multi‑head implementation suitable for GPT‑style models. ::: {.callout-note collapse="true" title="Full summary"} Learners end the unit able to both explain and implement the attention mechanism used in modern transformer decoders. You will understand why RNN encoder‑decoder architectures suffer an information bottleneck and how attention removes it by letting every output position attend directly to relevant input positions. Starting from raw token embeddings, you will work through the three arithmetic steps of attention for a single query—score, normalize, sum—implemented without trainable parameters to reveal the mechanics. From there you will vectorize the per‑token loop into two matrix operations that compute context vectors for all tokens at once. You will introduce trainable query, key and value projections and the 1/sqrt(d_k) scaling that stabilizes gradients, and learn how to package the whole computation as a reusable PyTorch layer using nn.Linear rather than raw parameters. The unit shows how to make attention causal by masking future positions (setting their scores to −∞), so the layer can be used for next‑token prediction, and how to add dropout on attention weights while accounting for the rescaling needed to preserve expected activations. You will implement attention that accepts batched inputs with shape (batch, tokens, dimension), store masks as buffers, and assemble masking, dropout and batching into a CausalAttention module. Finally you will build multi‑head causal attention: first the straightforward approach of running parallel heads and concatenating, then the efficient production form that projects once, reshapes to split heads, and batches the matrix multiplies. By the end you can write a production‑quality MultiHeadAttention module compatible with GPT‑style decoders and you will be prepared to use it as the core layer when implementing a full autoregressive language model. ::: ## Materials ::: {.hg-deck} <a class="hg-card" href="notes.qmd">Lecture notesThe unit written out, to read at your own pacever. 1.2.0 · 2026-09-02 16:27:30Open →</a> <a class="hg-card" href="slides.qmd">SlidesThe deck to present fromver. 1.0.0 · 2026-08-25 14:45:14Open →</a> ::: ## Source document - Building a Large Language Model (from scratch), _Sebastian Raschka_, 2024, Manning Books — [Link](https://www.manning.com/books/build-a-large-language-model-from-scratch) — Page 72-113 ::: {.hg-nav} [← 2 Working with text data](../2_working_with_text_data/index.qmd){.hg-prev} [4 Implementing a GPT model from scratch to generate text →](../4_implementing_a_gpt_model_from_scratch_to_generate_text/index.qmd){.hg-next} :::